The Long Road to Computational Location Privacy: A Survey

Abstract

The widespread adoption of continuously connected smartphones and tablets developed the usage of mobile applications, among which many use location to provide geolocated services. These services provide new prospects for users: getting directions to work in the morning, leaving a check-in at a restaurant at noon and checking next day's weather in the evening are possible right from any mobile device embedding a GPS chip. In these location-based applications, the user's location is sent to a server, which uses them to provide contextual and personalised answers. However, nothing prevents the latter from gathering, analysing and possibly sharing the collected information, which opens the door to many privacy threats. Indeed, mobility data can reveal sensitive information about users, among which one's home, work place or even religious and political preferences. For this reason, many privacy-preserving mechanisms have been proposed these last years to enhance location privacy while using geolocated services. This article surveys and organises contributions in this area from classical building blocks to the most recent developments of privacy threats and location privacy-preserving mechanisms. We divide the protection mechanisms between online and offline use cases, and organise them into six categories depending on the nature of their algorithm. Moreover, this article surveys the evaluation metrics used to assess protection mechanisms in terms of privacy, utility and performance. Finally, open challenges and new directions to address the problem of computational location privacy are pointed out and discussed.

Please Forget Where I Was Last Summer: The Privacy Risks of Public Location (Meta)Data

“…LPAuditor offers a comprehensive analysis of the privacy loss caused by location metadata by also exploring whether the remaining locations can be used to infer personal information that is typically considered sensitive. While the inference of sensitive information has been one of the main motivations behind prior research on location-privacy [61], such automated attacks have not been demonstrated in practice. Our system examines tweets that place the user at (or in close proximity of) locations that are associated with such information.…”

Improving energy efficiency in information‐centric mobile ad‐hoc networks using places of interest while respecting privacy

“…A major technique widely used for showing amount of privacy is k‐anonymity. () It states that at least k‐1 other users should have the same property as the one which the user is exposing (POI in our work). As the framework we are working on is a collection of mobiles transferring data between themselves using WiFi, they cannot be too far from each other because no communication will be achieved if they are far.…”

Re-identificação de trajetórias de veículos baseada na caracterização das preferências de caminho

“…Diante deste fato, diversas técnicas de proteçãoà privacidade têm sido propostas, como os algoritmos de anonimização baseados em pseudônimos. Um pseudônimoé um mecanismo de proteção a privacidade que consiste em substituir dados reais e sensitivos por identificadores que não tenham nenhuma associação com estes dados reais (Primault et al, 2018). Por exemplo, durante a publicação dos dados de localização de um veículo, as informações sensíveis como as placas dos veículos são substituídas por identificadores com o intuito de desassociar a identidade real do veículo 1 .…”

“…A re-identificaçãoé uma das abordagens de ataqueà privacidade que parte do princípio de identificar trajetórias anonimizadas e, consequentemente, conhecer as identidades a partir de um conjunto limitado de informações sobre a entidade-alvo (Primault, Boutet, Mokhtar, & Brunie, 2018). Esseé um ataque que também pode ser a "porta de entrada" para outros que têm como alvo uma entidade específica.…”